With the launch of several Lunar missions, such as Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be and are being acquired, and will need to be analyzed. When registering or analyzing lunar data, significant features need to be extracted from the image data. Planetary features, such as rocks, boulders, craters or ridges, are then used for applications such as: registering multi-temporal, multi-sensor, multi-view images; creating an obstacle distribution map for site selection or path planning purposes; or performing terrain categorization. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics.
The LRO is a NASA mission, aimed at creating a comprehensive atlas of the Moon features and resources to aid in the design of a lunar outpost and to prepare exploration and scientific missions to the Moon. LRO is scheduled to spend at least one year in orbit collecting detailed information about the Moon and its environment. Different types of data will be collected by LRO (and other Moon missions) at different times, by different sensors, and from different view-points. Therefore, registration will be used to jointly exploit, integrate, or compare these different data, and feature extraction is the first step to not only image registration, but also any further analysis of these data.
The identification of the features that are present on the planetary surface by a human expert is a time-consuming endeavor. Therefore, a trustworthy automatic procedure to detect the position, structure, and dimension of each feature is highly desirable. This is a difficult task because limited data are available, the quality of the images is generally low (i.e., it depends on illumination and surface properties), and the features that are present in the images can be barely visible due to erosion and exhibit different structures and variable sizes.
Among typical features in Lunar- and planet-surface imagery, craters play a primary role. The crater detection problem has been widely addressed and different approaches have been proposed in the literature. The image-based approaches for crater detection can be divided into two main categories: supervised and unsupervised. The supervised methods require the input of an expert and generally use machine learning concepts to train the algorithm to feature extraction. Unsupervised methods are completely automatic and are generally based on pattern recognition techniques. Different approaches have been proposed, based on template matching, texture analysis, neural networks, or a combination of these techniques. Template matching has been described in A. Flores-Mendez, “Crater marking and classification using computer vision,” in Progress in Pattern Recognition, Speech and Image Analysis, vol. 2905, Lecture Notes in Computer Science. New York: Springer-Verlag, 2003, pp. 79-86, which is incorporated herein in its entirety by reference. Texture analysis has been described in J. R. Kim, J.-P. Muller, S. van Gasselt, J. G. Morley, and G. Neukum, “Automated crater detection, a new tool for Mars cartography and chronology,” Photogramm. Eng. Remote Sensing, vol. 71, no. 10, pp. 13-22, 2000, which is incorporated herein in its entirety by reference. Neural networks have been described in A. A. Smirnov, “Exploratory study of automated crater detection algorithm,” Boulder, Colo., 2002. Tech. Rep., which is incorporated herein in its entirety by reference.
Compared to Earth Science remote sensing data, lunar images usually present very low contrast and uneven illumination. The boundary of lunar features is not well defined, and it is therefore somewhat difficult to segment and characterize lunar images. Also, because of uneven illumination, edges extracted from lunar images do not form closed contours, and post-processing needs to be done to link these edges. Further, because regions are difficult to characterize due to lack of contrast, if a method such as region growing is used, one level of iteration is not sufficient to describe all the features. With the large number of new lunar data that will be collected in the next few years, it is important to design an automated method to extract these features, and to perform tasks such as image registration. As such, an automated and robust feature extraction method for lunar images is needed.